Improving Estimation of Fiber Orientations in Diffusion MRI Using Inter-Subject Information Sharing

• Published in: Scientific reports Vol. 6; no. 1; p. 37847
• Main Authors: Chen, Geng, Zhang, Pei, Li, Ke, Wee, Chong-Yaw, Wu, Yafeng, Shen, Dinggang, Yap, Pew-Thian
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 28.11.2016; Nature Publishing Group
• Subjects: 631/114/1314; 631/114/1564; Brain - diagnostic imaging; Databases, Factual; Diffusion; Diffusion Magnetic Resonance Imaging - methods; Diffusion Tensor Imaging - methods; Diffusion Tensor Imaging - standards; Humanities and Social Sciences; Humans; Image Processing, Computer-Assisted - methods; Magnetic resonance imaging; multidisciplinary; Neural networks; Neuroimaging; NMR; Noise reduction; Nuclear magnetic resonance; Reference Standards; Science; Signal-To-Noise Ratio
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/srep37847

Diffusion magnetic resonance imaging is widely used to investigate diffusion patterns of water molecules in the human brain. It provides information that is useful for tracing axonal bundles and inferring brain connectivity. Diffusion axonal tracing, namely tractography, relies on local directional information provided by the orientation distribution functions (ODFs) estimated at each voxel. To accurately estimate ODFs, data of good signal-to-noise ratio and sufficient angular samples are desired. This is however not always available in practice. In this paper, we propose to improve ODF estimation by using inter-subject image correlation. Specifically, we demonstrate that diffusion-weighted images acquired from different subjects can be transformed to the space of a target subject to drastically increase the number of angular samples to improve ODF estimation. This is largely due to the incoherence of the angular samples generated when the diffusion signals are reoriented and warped to the target space. To reorient the diffusion signals, we propose a new spatial normalization method that directly acts on diffusion signals using local affine transforms. Experiments on both synthetic data and real data show that our method can reduce noise-induced artifacts, such as spurious ODF peaks, and yield more coherent orientations.